eprintid: 16236
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/62/36
datestamp: 2023-12-19 03:22:46
lastmod: 2023-12-19 03:22:46
status_changed: 2023-12-19 03:05:53
type: article
metadata_visibility: show
creators_name: Junejo, A.Z.
creators_name: Hashmani, M.A.
creators_name: Alabdulatif, A.A.
creators_name: Memon, M.M.
creators_name: Jaffari, S.R.
creators_name: Abdullah, M.N.B.
title: RZee: Cryptographic and statistical model for adversary detection and filtration to preserve blockchain privacy
ispublished: pub
note: cited By 2
abstract: Preserving anonymity and confidentiality of transactions has become crucial with widespread of the blockchain technology. Despite of the increased efforts for retaining privacy in blockchain networks, invasion attacks are still surfacing. Most of these attacks do not come from outsiders, but from the resident adversarial nodes. Existence of these insider adversaries lead to damaging of an organization's internal network system and information leakage. Consequently, transaction anonymity and confidentiality are compromised. Hence, adversary detection and filtration play a vital role in protecting networks against unforeseen privacy and security threats. Therefore, in this paper, we propose RZee, a cryptographic and statistical privacy preserving model for adversary detection and filtering in blockchain networks. Firstly, RZee exploits zero-knowledge proofs to cryptographically secure the data. Secondly, based on certain identified conditions, RZee captures node behavior and blacklists malicious nodes to restrict those from injecting harmful data into the chain or viewing transactions as they propagate across the network. This adds an additional layer of protecting transactions from unauthorized and malicious intervention. The proposed framework is evaluated based on various privacy attributes as identified by literature. For this evaluation, 4 different types of experiments have been conducted. Further, the comparison of privacy perseverance of RZee with existing benchmark privacy-preserving frameworks is also done. The results depict that performance and privacy preservation in RZee exceeds the rest with an attribute score of 6.774 and a gain margin of 46.5. © 2022 The Author(s)
date: 2022
publisher: King Saud bin Abdulaziz University
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141106235&doi=10.1016%2fj.jksuci.2022.07.007&partnerID=40&md5=95735e01beb6160af5d268202b222922
id_number: 10.1016/j.jksuci.2022.07.007
full_text_status: none
publication: Journal of King Saud University - Computer and Information Sciences
volume: 34
number: 10
pagerange: 7885-7910
refereed: TRUE
issn: 13191578
citation:   Junejo, A.Z. and Hashmani, M.A. and Alabdulatif, A.A. and Memon, M.M. and Jaffari, S.R. and Abdullah, M.N.B.  (2022) RZee: Cryptographic and statistical model for adversary detection and filtration to preserve blockchain privacy.  Journal of King Saud University - Computer and Information Sciences, 34 (10).  pp. 7885-7910.  ISSN 13191578